class_docstr: Circular (Morgan) fingerprints.
  
  Extended Connectivity Circular Fingerprints compute a bag-of-words style
  representation of a molecule by breaking it into local neighborhoods and
  hashing into a bit vector of the specified size. It is used specifically
  for structure-activity modelling. See [1]_ for more details.
  
  References
  ----------
  .. [1] Rogers, David, and Mathew Hahn. "Extended-connectivity fingerprints."
      Journal of chemical information and modeling 50.5 (2010): 742-754.
  
  Note
  ----
  This class requires RDKit to be installed.
  
  Examples
  --------
  >>> import deepchem as dc
  >>> from rdkit import Chem
  >>> smiles = ['C1=CC=CC=C1']
  >>> # Example 1: (size = 2048, radius = 4)
  >>> featurizer = dc.feat.CircularFingerprint(size=2048, radius=4)
  >>> features = featurizer.featurize(smiles)
  >>> type(features[0])
  <class 'numpy.ndarray'>
  >>> features[0].shape
  (2048,)
  
  >>> # Example 2: (size = 2048, radius = 4, sparse = True, smiles = True)
  >>> featurizer = dc.feat.CircularFingerprint(size=2048, radius=8,
  ...                                          sparse=True, smiles=True)
  >>> features = featurizer.featurize(smiles)
  >>> type(features[0]) # dict containing fingerprints
  <class 'dict'>
docstr: Parameters
  ----------
  radius: int, optional (default 2)
      Fingerprint radius.
  size: int, optional (default 2048)
      Length of generated bit vector.
  chiral: bool, optional (default False)
      Whether to consider chirality in fingerprint generation.
  bonds: bool, optional (default True)
      Whether to consider bond order in fingerprint generation.
  features: bool, optional (default False)
      Whether to use feature information instead of atom information; see
      RDKit docs for more info.
  sparse: bool, optional (default False)
      Whether to return a dict for each molecule containing the sparse
      fingerprint.
  smiles: bool, optional (default False)
      Whether to calculate SMILES strings for fragment IDs (only applicable
      when calculating sparse fingerprints).
  is_counts_based: bool, optional (default False)
      Whether to generates a counts-based fingerprint.
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code: def __init__(
      self,
      radius: int = 2,
      size: int = 2048,
      chiral: bool = False,
      bonds: bool = True,
      features: bool = False,
      sparse: bool = False,
      smiles: bool = False,
      is_counts_based: bool = False,
  ):
      """
      Parameters
      ----------
      radius: int, optional (default 2)
          Fingerprint radius.
      size: int, optional (default 2048)
          Length of generated bit vector.
      chiral: bool, optional (default False)
          Whether to consider chirality in fingerprint generation.
      bonds: bool, optional (default True)
          Whether to consider bond order in fingerprint generation.
      features: bool, optional (default False)
          Whether to use feature information instead of atom information; see
          RDKit docs for more info.
      sparse: bool, optional (default False)
          Whether to return a dict for each molecule containing the sparse
          fingerprint.
      smiles: bool, optional (default False)
          Whether to calculate SMILES strings for fragment IDs (only applicable
          when calculating sparse fingerprints).
      is_counts_based: bool, optional (default False)
          Whether to generates a counts-based fingerprint.
  
      """
      self.radius = radius
      self.size = size
      self.chiral = chiral
      self.bonds = bonds
      self.features = features
      self.sparse = sparse
      self.smiles = smiles
      self.is_counts_based = is_counts_based
  
file_path: deepchem/deepchem/feat/molecule_featurizers/circular_fingerprint.py
imports: from typing import Dict
  import numpy
  from deepchem.utils.typing import RDKitMol
  from deepchem.feat.base_classes import MolecularFeaturizer
label_desc: Use image node_id 1 to create a new CircularFingerprint object from inherited base classes: MolecularFeaturizer with example: obj = CircularFingerprint(radius, size, chiral, bonds, features, sparse, smiles, is_counts_based)